Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations5000
Missing cells3023
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory664.8 B

Variable types

Categorical12
Numeric15

Alerts

Log_Mileage is highly overall correlated with MileageHigh correlation
Log_Price is highly overall correlated with PriceHigh correlation
Mileage is highly overall correlated with Log_MileageHigh correlation
Price is highly overall correlated with Log_PriceHigh correlation
Modification has 3023 (60.5%) missing values Missing

Reproduction

Analysis started2025-03-02 10:01:26.965986
Analysis finished2025-03-02 10:02:07.268694
Duration40.3 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Brand
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size275.1 KiB
Ferrari
533 
Chevrolet
516 
Aston Martin
513 
Porsche
513 
Bugatti
507 
Other values (5)
2418 

Length

Max length12
Median length11
Mean length7.311
Min length3

Characters and Unicode

Total characters36555
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNissan
2nd rowMcLaren
3rd rowChevrolet
4th rowBugatti
5th rowNissan

Common Values

ValueCountFrequency (%)
Ferrari 533
10.7%
Chevrolet 516
10.3%
Aston Martin 513
10.3%
Porsche 513
10.3%
Bugatti 507
10.1%
BMW 496
9.9%
Ford 486
9.7%
McLaren 486
9.7%
Nissan 480
9.6%
Lamborghini 470
9.4%

Length

2025-03-02T15:32:07.472171image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:07.662325image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
ferrari 533
9.7%
chevrolet 516
9.4%
aston 513
9.3%
martin 513
9.3%
porsche 513
9.3%
bugatti 507
9.2%
bmw 496
9.0%
ford 486
8.8%
mclaren 486
8.8%
nissan 480
8.7%

Most occurring characters

ValueCountFrequency (%)
r 4583
12.5%
a 2989
 
8.2%
i 2973
 
8.1%
e 2564
 
7.0%
t 2556
 
7.0%
o 2498
 
6.8%
n 2462
 
6.7%
s 1986
 
5.4%
h 1499
 
4.1%
M 1495
 
4.1%
Other values (17) 10950
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29051
79.5%
Uppercase Letter 6991
 
19.1%
Space Separator 513
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4583
15.8%
a 2989
10.3%
i 2973
10.2%
e 2564
8.8%
t 2556
8.8%
o 2498
8.6%
n 2462
8.5%
s 1986
6.8%
h 1499
 
5.2%
c 999
 
3.4%
Other values (7) 3942
13.6%
Uppercase Letter
ValueCountFrequency (%)
M 1495
21.4%
F 1019
14.6%
B 1003
14.3%
L 956
13.7%
C 516
 
7.4%
A 513
 
7.3%
P 513
 
7.3%
W 496
 
7.1%
N 480
 
6.9%
Space Separator
ValueCountFrequency (%)
513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36042
98.6%
Common 513
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4583
12.7%
a 2989
 
8.3%
i 2973
 
8.2%
e 2564
 
7.1%
t 2556
 
7.1%
o 2498
 
6.9%
n 2462
 
6.8%
s 1986
 
5.5%
h 1499
 
4.2%
M 1495
 
4.1%
Other values (16) 10437
29.0%
Common
ValueCountFrequency (%)
513
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4583
12.5%
a 2989
 
8.2%
i 2973
 
8.1%
e 2564
 
7.0%
t 2556
 
7.0%
o 2498
 
6.8%
n 2462
 
6.7%
s 1986
 
5.4%
h 1499
 
4.1%
M 1495
 
4.1%
Other values (17) 10950
30.0%

Model
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size277.4 KiB
M4 Competition
538 
GT-R
537 
488 GTB
520 
Huracan
515 
DBS
508 
Other values (5)
2382 

Length

Max length14
Median length11
Mean length7.7848
Min length3

Characters and Unicode

Total characters38924
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row720S
2nd row911 Turbo S
3rd rowM4 Competition
4th rowChiron
5th rowChiron

Common Values

ValueCountFrequency (%)
M4 Competition 538
10.8%
GT-R 537
10.7%
488 GTB 520
10.4%
Huracan 515
10.3%
DBS 508
10.2%
Mustang GT 508
10.2%
720S 485
9.7%
911 Turbo S 483
9.7%
Chiron 455
9.1%
Corvette Z06 451
9.0%

Length

2025-03-02T15:32:07.995221image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:08.160000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
m4 538
 
6.7%
competition 538
 
6.7%
gt-r 537
 
6.7%
488 520
 
6.5%
gtb 520
 
6.5%
huracan 515
 
6.5%
dbs 508
 
6.4%
mustang 508
 
6.4%
gt 508
 
6.4%
720s 485
 
6.1%
Other values (6) 2806
35.1%

Most occurring characters

ValueCountFrequency (%)
2983
 
7.7%
t 2486
 
6.4%
o 2465
 
6.3%
T 2048
 
5.3%
n 2016
 
5.2%
r 1904
 
4.9%
G 1565
 
4.0%
a 1538
 
4.0%
i 1531
 
3.9%
u 1506
 
3.9%
Other values (26) 18882
48.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18882
48.5%
Uppercase Letter 10618
27.3%
Decimal Number 5904
 
15.2%
Space Separator 2983
 
7.7%
Dash Punctuation 537
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2486
13.2%
o 2465
13.1%
n 2016
10.7%
r 1904
10.1%
a 1538
8.1%
i 1531
8.1%
u 1506
8.0%
e 1440
7.6%
p 538
 
2.8%
m 538
 
2.8%
Other values (6) 2920
15.5%
Uppercase Letter
ValueCountFrequency (%)
T 2048
19.3%
G 1565
14.7%
S 1476
13.9%
C 1444
13.6%
M 1046
9.9%
B 1028
9.7%
R 537
 
5.1%
H 515
 
4.9%
D 508
 
4.8%
Z 451
 
4.2%
Decimal Number
ValueCountFrequency (%)
4 1058
17.9%
8 1040
17.6%
1 966
16.4%
0 936
15.9%
2 485
8.2%
7 485
8.2%
9 483
8.2%
6 451
7.6%
Space Separator
ValueCountFrequency (%)
2983
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29500
75.8%
Common 9424
 
24.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2486
 
8.4%
o 2465
 
8.4%
T 2048
 
6.9%
n 2016
 
6.8%
r 1904
 
6.5%
G 1565
 
5.3%
a 1538
 
5.2%
i 1531
 
5.2%
u 1506
 
5.1%
S 1476
 
5.0%
Other values (16) 10965
37.2%
Common
ValueCountFrequency (%)
2983
31.7%
4 1058
 
11.2%
8 1040
 
11.0%
1 966
 
10.3%
0 936
 
9.9%
- 537
 
5.7%
2 485
 
5.1%
7 485
 
5.1%
9 483
 
5.1%
6 451
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2983
 
7.7%
t 2486
 
6.4%
o 2465
 
6.3%
T 2048
 
5.3%
n 2016
 
5.2%
r 1904
 
4.9%
G 1565
 
4.0%
a 1538
 
4.0%
i 1531
 
3.9%
u 1506
 
3.9%
Other values (26) 18882
48.5%

Year
Real number (ℝ)

Distinct45
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.9048
Minimum1980
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:08.478472image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1982
Q11991
median2002
Q32013
95-th percentile2022
Maximum2024
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.873697
Coefficient of variation (CV)0.0064307241
Kurtosis-1.1704012
Mean2001.9048
Median Absolute Deviation (MAD)11
Skewness0.037564837
Sum10009524
Variance165.73208
MonotonicityNot monotonic
2025-03-02T15:32:08.803233image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1993 131
 
2.6%
2023 131
 
2.6%
1994 129
 
2.6%
1996 127
 
2.5%
1992 126
 
2.5%
1995 123
 
2.5%
2007 122
 
2.4%
2001 121
 
2.4%
2002 120
 
2.4%
2006 120
 
2.4%
Other values (35) 3750
75.0%
ValueCountFrequency (%)
1980 100
2.0%
1981 108
2.2%
1982 107
2.1%
1983 108
2.2%
1984 105
2.1%
1985 114
2.3%
1986 110
2.2%
1987 118
2.4%
1988 105
2.1%
1989 115
2.3%
ValueCountFrequency (%)
2024 115
2.3%
2023 131
2.6%
2022 90
1.8%
2021 107
2.1%
2020 109
2.2%
2019 111
2.2%
2018 113
2.3%
2017 107
2.1%
2016 110
2.2%
2015 113
2.3%

Country
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.6 KiB
Asia
1677 
Europe
1676 
USA
1647 

Length

Max length6
Median length4
Mean length4.341
Min length3

Characters and Unicode

Total characters21705
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowUSA
4th rowAsia
5th rowEurope

Common Values

ValueCountFrequency (%)
Asia 1677
33.5%
Europe 1676
33.5%
USA 1647
32.9%

Length

2025-03-02T15:32:08.993227image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:09.112248image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
asia 1677
33.5%
europe 1676
33.5%
usa 1647
32.9%

Most occurring characters

ValueCountFrequency (%)
A 3324
15.3%
s 1677
7.7%
i 1677
7.7%
a 1677
7.7%
E 1676
7.7%
u 1676
7.7%
r 1676
7.7%
o 1676
7.7%
p 1676
7.7%
e 1676
7.7%
Other values (2) 3294
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13411
61.8%
Uppercase Letter 8294
38.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1677
12.5%
i 1677
12.5%
a 1677
12.5%
u 1676
12.5%
r 1676
12.5%
o 1676
12.5%
p 1676
12.5%
e 1676
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 3324
40.1%
E 1676
20.2%
U 1647
19.9%
S 1647
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 21705
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3324
15.3%
s 1677
7.7%
i 1677
7.7%
a 1677
7.7%
E 1676
7.7%
u 1676
7.7%
r 1676
7.7%
o 1676
7.7%
p 1676
7.7%
e 1676
7.7%
Other values (2) 3294
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3324
15.3%
s 1677
7.7%
i 1677
7.7%
a 1677
7.7%
E 1676
7.7%
u 1676
7.7%
r 1676
7.7%
o 1676
7.7%
p 1676
7.7%
e 1676
7.7%
Other values (2) 3294
15.2%

Condition
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size258.3 KiB
new
2559 
used
1824 
salvage
495 
restored
 
122

Length

Max length8
Median length3
Mean length3.8828
Min length3

Characters and Unicode

Total characters19414
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowused
2nd rownew
3rd rownew
4th rowused
5th rownew

Common Values

ValueCountFrequency (%)
new 2559
51.2%
used 1824
36.5%
salvage 495
 
9.9%
restored 122
 
2.4%

Length

2025-03-02T15:32:09.274149image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:09.376590image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
new 2559
51.2%
used 1824
36.5%
salvage 495
 
9.9%
restored 122
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 5122
26.4%
n 2559
13.2%
w 2559
13.2%
s 2441
12.6%
d 1946
 
10.0%
u 1824
 
9.4%
a 990
 
5.1%
l 495
 
2.5%
v 495
 
2.5%
g 495
 
2.5%
Other values (3) 488
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19414
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5122
26.4%
n 2559
13.2%
w 2559
13.2%
s 2441
12.6%
d 1946
 
10.0%
u 1824
 
9.4%
a 990
 
5.1%
l 495
 
2.5%
v 495
 
2.5%
g 495
 
2.5%
Other values (3) 488
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 19414
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5122
26.4%
n 2559
13.2%
w 2559
13.2%
s 2441
12.6%
d 1946
 
10.0%
u 1824
 
9.4%
a 990
 
5.1%
l 495
 
2.5%
v 495
 
2.5%
g 495
 
2.5%
Other values (3) 488
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5122
26.4%
n 2559
13.2%
w 2559
13.2%
s 2441
12.6%
d 1946
 
10.0%
u 1824
 
9.4%
a 990
 
5.1%
l 495
 
2.5%
v 495
 
2.5%
g 495
 
2.5%
Other values (3) 488
 
2.5%

Engine_Size
Real number (ℝ)

Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8287
Minimum1.6
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:09.542898image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.9
Q13.2
median4.8
Q36.5
95-th percentile7.7
Maximum8
Range6.4
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation1.8583533
Coefficient of variation (CV)0.38485582
Kurtosis-1.2222631
Mean4.8287
Median Absolute Deviation (MAD)1.6
Skewness-0.0027822475
Sum24143.5
Variance3.453477
MonotonicityNot monotonic
2025-03-02T15:32:09.742957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 97
 
1.9%
6.7 97
 
1.9%
6.5 94
 
1.9%
7.3 93
 
1.9%
6.1 92
 
1.8%
2.3 91
 
1.8%
7.5 91
 
1.8%
3.7 90
 
1.8%
7.4 90
 
1.8%
7.7 90
 
1.8%
Other values (55) 4075
81.5%
ValueCountFrequency (%)
1.6 37
0.7%
1.7 82
1.6%
1.8 64
1.3%
1.9 75
1.5%
2 81
1.6%
2.1 71
1.4%
2.2 69
1.4%
2.3 91
1.8%
2.4 68
1.4%
2.5 79
1.6%
ValueCountFrequency (%)
8 50
1.0%
7.9 66
1.3%
7.8 83
1.7%
7.7 90
1.8%
7.6 71
1.4%
7.5 91
1.8%
7.4 90
1.8%
7.3 93
1.9%
7.2 78
1.6%
7.1 71
1.4%

Horsepower
Real number (ℝ)

Distinct1363
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean822.8916
Minimum130
Maximum1521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:09.928888image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile203
Q1472
median815.5
Q31176
95-th percentile1450
Maximum1521
Range1391
Interquartile range (IQR)704

Descriptive statistics

Standard deviation401.36255
Coefficient of variation (CV)0.48774656
Kurtosis-1.2117037
Mean822.8916
Median Absolute Deviation (MAD)350.5
Skewness0.011814728
Sum4114458
Variance161091.9
MonotonicityNot monotonic
2025-03-02T15:32:10.148696image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1077 12
 
0.2%
1258 11
 
0.2%
1310 11
 
0.2%
211 11
 
0.2%
1333 11
 
0.2%
1242 11
 
0.2%
670 10
 
0.2%
646 9
 
0.2%
456 9
 
0.2%
359 9
 
0.2%
Other values (1353) 4896
97.9%
ValueCountFrequency (%)
130 5
0.1%
131 2
 
< 0.1%
132 4
0.1%
133 2
 
< 0.1%
134 3
0.1%
135 5
0.1%
136 4
0.1%
137 2
 
< 0.1%
138 1
 
< 0.1%
139 3
0.1%
ValueCountFrequency (%)
1521 3
0.1%
1520 2
 
< 0.1%
1519 1
 
< 0.1%
1518 2
 
< 0.1%
1517 3
0.1%
1516 3
0.1%
1515 3
0.1%
1514 7
0.1%
1513 3
0.1%
1512 7
0.1%

Torque
Real number (ℝ)

Distinct1561
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean938.8006
Minimum120
Maximum1758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:10.343314image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile204
Q1522
median948
Q31345
95-th percentile1673
Maximum1758
Range1638
Interquartile range (IQR)823

Descriptive statistics

Standard deviation472.95428
Coefficient of variation (CV)0.50378566
Kurtosis-1.2046801
Mean938.8006
Median Absolute Deviation (MAD)410
Skewness-0.0129877
Sum4694003
Variance223685.75
MonotonicityNot monotonic
2025-03-02T15:32:10.559578image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
557 10
 
0.2%
333 9
 
0.2%
438 9
 
0.2%
258 9
 
0.2%
1621 9
 
0.2%
911 9
 
0.2%
544 9
 
0.2%
469 9
 
0.2%
149 9
 
0.2%
1554 8
 
0.2%
Other values (1551) 4910
98.2%
ValueCountFrequency (%)
120 2
 
< 0.1%
121 2
 
< 0.1%
122 3
0.1%
123 4
0.1%
124 2
 
< 0.1%
125 7
0.1%
126 2
 
< 0.1%
127 2
 
< 0.1%
128 3
0.1%
129 6
0.1%
ValueCountFrequency (%)
1758 4
0.1%
1757 1
 
< 0.1%
1756 2
 
< 0.1%
1755 3
0.1%
1754 5
0.1%
1753 2
 
< 0.1%
1752 3
0.1%
1751 5
0.1%
1750 7
0.1%
1749 3
0.1%

Weight
Real number (ℝ)

Distinct1527
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1689.7242
Minimum900
Maximum2499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:10.993458image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile977.95
Q11286
median1684
Q32101
95-th percentile2418
Maximum2499
Range1599
Interquartile range (IQR)815

Descriptive statistics

Standard deviation465.78642
Coefficient of variation (CV)0.27565825
Kurtosis-1.2175191
Mean1689.7242
Median Absolute Deviation (MAD)407
Skewness0.022238638
Sum8448621
Variance216956.99
MonotonicityNot monotonic
2025-03-02T15:32:11.192384image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1530 10
 
0.2%
2208 9
 
0.2%
2433 9
 
0.2%
1440 9
 
0.2%
2181 9
 
0.2%
2444 9
 
0.2%
1009 9
 
0.2%
1050 9
 
0.2%
1816 8
 
0.2%
1456 8
 
0.2%
Other values (1517) 4911
98.2%
ValueCountFrequency (%)
900 1
 
< 0.1%
901 1
 
< 0.1%
902 3
0.1%
903 4
0.1%
904 1
 
< 0.1%
905 7
0.1%
906 2
 
< 0.1%
907 2
 
< 0.1%
908 2
 
< 0.1%
909 7
0.1%
ValueCountFrequency (%)
2499 3
0.1%
2498 2
 
< 0.1%
2497 1
 
< 0.1%
2496 1
 
< 0.1%
2495 3
0.1%
2494 3
0.1%
2493 6
0.1%
2492 2
 
< 0.1%
2491 3
0.1%
2489 3
0.1%

Top_Speed
Real number (ℝ)

Distinct250
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.857
Minimum150
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:11.409716image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile162
Q1214
median275
Q3337
95-th percentile386
Maximum399
Range249
Interquartile range (IQR)123

Descriptive statistics

Standard deviation72.062214
Coefficient of variation (CV)0.26218075
Kurtosis-1.1904929
Mean274.857
Median Absolute Deviation (MAD)62
Skewness-0.0072210469
Sum1374285
Variance5192.9627
MonotonicityNot monotonic
2025-03-02T15:32:11.614790image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173 35
 
0.7%
230 33
 
0.7%
301 32
 
0.6%
299 30
 
0.6%
255 30
 
0.6%
335 30
 
0.6%
157 29
 
0.6%
383 29
 
0.6%
378 28
 
0.6%
242 28
 
0.6%
Other values (240) 4696
93.9%
ValueCountFrequency (%)
150 26
0.5%
151 20
0.4%
152 19
0.4%
153 27
0.5%
154 16
0.3%
155 15
0.3%
156 18
0.4%
157 29
0.6%
158 19
0.4%
159 15
0.3%
ValueCountFrequency (%)
399 17
0.3%
398 17
0.3%
397 22
0.4%
396 25
0.5%
395 24
0.5%
394 16
0.3%
393 20
0.4%
392 17
0.3%
391 13
0.3%
390 8
 
0.2%

Acceleration_0_100
Real number (ℝ)

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.51728
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:11.811838image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.3
Q13.3
median4.5
Q35.8
95-th percentile6.8
Maximum7
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.4484726
Coefficient of variation (CV)0.32065151
Kurtosis-1.1942962
Mean4.51728
Median Absolute Deviation (MAD)1.2
Skewness0.011302362
Sum22586.4
Variance2.098073
MonotonicityNot monotonic
2025-03-02T15:32:12.020612image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 127
 
2.5%
3.9 118
 
2.4%
4.4 117
 
2.3%
2.4 117
 
2.3%
3.4 117
 
2.3%
4.5 114
 
2.3%
6.8 114
 
2.3%
6.9 113
 
2.3%
4.6 112
 
2.2%
6 111
 
2.2%
Other values (41) 3840
76.8%
ValueCountFrequency (%)
2 35
 
0.7%
2.1 99
2.0%
2.2 94
1.9%
2.3 104
2.1%
2.4 117
2.3%
2.5 97
1.9%
2.6 103
2.1%
2.7 98
2.0%
2.8 86
1.7%
2.9 87
1.7%
ValueCountFrequency (%)
7 50
 
1.0%
6.9 113
2.3%
6.8 114
2.3%
6.7 127
2.5%
6.6 105
2.1%
6.5 97
1.9%
6.4 90
1.8%
6.3 92
1.8%
6.2 95
1.9%
6.1 87
1.7%

Fuel_Type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size271.9 KiB
Petrol
1688 
Diesel
1684 
Electric
1628 

Length

Max length8
Median length6
Mean length6.6512
Min length6

Characters and Unicode

Total characters33256
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowElectric
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Petrol 1688
33.8%
Diesel 1684
33.7%
Electric 1628
32.6%

Length

2025-03-02T15:32:12.211946image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:12.328486image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
petrol 1688
33.8%
diesel 1684
33.7%
electric 1628
32.6%

Most occurring characters

ValueCountFrequency (%)
e 6684
20.1%
l 5000
15.0%
t 3316
10.0%
r 3316
10.0%
i 3312
10.0%
c 3256
9.8%
P 1688
 
5.1%
o 1688
 
5.1%
D 1684
 
5.1%
s 1684
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28256
85.0%
Uppercase Letter 5000
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6684
23.7%
l 5000
17.7%
t 3316
11.7%
r 3316
11.7%
i 3312
11.7%
c 3256
11.5%
o 1688
 
6.0%
s 1684
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
P 1688
33.8%
D 1684
33.7%
E 1628
32.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 33256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6684
20.1%
l 5000
15.0%
t 3316
10.0%
r 3316
10.0%
i 3312
10.0%
c 3256
9.8%
P 1688
 
5.1%
o 1688
 
5.1%
D 1684
 
5.1%
s 1684
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6684
20.1%
l 5000
15.0%
t 3316
10.0%
r 3316
10.0%
i 3312
10.0%
c 3256
9.8%
P 1688
 
5.1%
o 1688
 
5.1%
D 1684
 
5.1%
s 1684
 
5.1%

Drivetrain
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size254.0 KiB
RWD
1681 
AWD
1677 
FWD
1642 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRWD
2nd rowRWD
3rd rowFWD
4th rowRWD
5th rowAWD

Common Values

ValueCountFrequency (%)
RWD 1681
33.6%
AWD 1677
33.5%
FWD 1642
32.8%

Length

2025-03-02T15:32:12.465865image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:12.562408image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
rwd 1681
33.6%
awd 1677
33.5%
fwd 1642
32.8%

Most occurring characters

ValueCountFrequency (%)
W 5000
33.3%
D 5000
33.3%
R 1681
 
11.2%
A 1677
 
11.2%
F 1642
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 5000
33.3%
D 5000
33.3%
R 1681
 
11.2%
A 1677
 
11.2%
F 1642
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 15000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 5000
33.3%
D 5000
33.3%
R 1681
 
11.2%
A 1677
 
11.2%
F 1642
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 5000
33.3%
D 5000
33.3%
R 1681
 
11.2%
A 1677
 
11.2%
F 1642
 
10.9%

Transmission
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
CVT
1267 
Automatic
1265 
DCT
1237 
Manual
1231 

Length

Max length9
Median length3
Mean length5.2566
Min length3

Characters and Unicode

Total characters26283
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomatic
2nd rowDCT
3rd rowAutomatic
4th rowCVT
5th rowDCT

Common Values

ValueCountFrequency (%)
CVT 1267
25.3%
Automatic 1265
25.3%
DCT 1237
24.7%
Manual 1231
24.6%

Length

2025-03-02T15:32:12.695725image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:12.812090image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
cvt 1267
25.3%
automatic 1265
25.3%
dct 1237
24.7%
manual 1231
24.6%

Most occurring characters

ValueCountFrequency (%)
a 3727
14.2%
t 2530
9.6%
T 2504
9.5%
C 2504
9.5%
u 2496
9.5%
V 1267
 
4.8%
A 1265
 
4.8%
o 1265
 
4.8%
m 1265
 
4.8%
i 1265
 
4.8%
Other values (5) 6195
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16275
61.9%
Uppercase Letter 10008
38.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3727
22.9%
t 2530
15.5%
u 2496
15.3%
o 1265
 
7.8%
m 1265
 
7.8%
i 1265
 
7.8%
c 1265
 
7.8%
n 1231
 
7.6%
l 1231
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
T 2504
25.0%
C 2504
25.0%
V 1267
12.7%
A 1265
12.6%
D 1237
12.4%
M 1231
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 26283
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3727
14.2%
t 2530
9.6%
T 2504
9.5%
C 2504
9.5%
u 2496
9.5%
V 1267
 
4.8%
A 1265
 
4.8%
o 1265
 
4.8%
m 1265
 
4.8%
i 1265
 
4.8%
Other values (5) 6195
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3727
14.2%
t 2530
9.6%
T 2504
9.5%
C 2504
9.5%
u 2496
9.5%
V 1267
 
4.8%
A 1265
 
4.8%
o 1265
 
4.8%
m 1265
 
4.8%
i 1265
 
4.8%
Other values (5) 6195
23.6%

Fuel_Efficiency
Real number (ℝ)

Distinct101
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.03834
Minimum5
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:12.981430image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.5
Q17.6
median10
Q312.6
95-th percentile14.5
Maximum15
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8816129
Coefficient of variation (CV)0.2870607
Kurtosis-1.1893539
Mean10.03834
Median Absolute Deviation (MAD)2.5
Skewness-0.02564631
Sum50191.7
Variance8.3036928
MonotonicityNot monotonic
2025-03-02T15:32:13.178536image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 66
 
1.3%
13.4 65
 
1.3%
9.2 65
 
1.3%
13.9 64
 
1.3%
10.5 64
 
1.3%
6.1 62
 
1.2%
13.1 61
 
1.2%
8.5 60
 
1.2%
8.2 60
 
1.2%
9.6 59
 
1.2%
Other values (91) 4374
87.5%
ValueCountFrequency (%)
5 34
0.7%
5.1 46
0.9%
5.2 56
1.1%
5.3 50
1.0%
5.4 56
1.1%
5.5 41
0.8%
5.6 45
0.9%
5.7 47
0.9%
5.8 49
1.0%
5.9 46
0.9%
ValueCountFrequency (%)
15 18
 
0.4%
14.9 47
0.9%
14.8 58
1.2%
14.7 56
1.1%
14.6 49
1.0%
14.5 37
0.7%
14.4 44
0.9%
14.3 45
0.9%
14.2 55
1.1%
14.1 57
1.1%

CO2_Emissions
Real number (ℝ)

Distinct350
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.6952
Minimum100
Maximum449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:13.393420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile117
Q1186
median273
Q3357
95-th percentile431
Maximum449
Range349
Interquartile range (IQR)171

Descriptive statistics

Standard deviation100.15425
Coefficient of variation (CV)0.36727542
Kurtosis-1.1731007
Mean272.6952
Median Absolute Deviation (MAD)85
Skewness0.019410817
Sum1363476
Variance10030.873
MonotonicityNot monotonic
2025-03-02T15:32:13.612816image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 27
 
0.5%
221 25
 
0.5%
122 24
 
0.5%
187 23
 
0.5%
307 23
 
0.5%
183 23
 
0.5%
449 22
 
0.4%
316 22
 
0.4%
357 22
 
0.4%
114 22
 
0.4%
Other values (340) 4767
95.3%
ValueCountFrequency (%)
100 8
 
0.2%
101 20
0.4%
102 15
0.3%
103 18
0.4%
104 12
0.2%
105 14
0.3%
106 8
 
0.2%
107 10
0.2%
108 14
0.3%
109 17
0.3%
ValueCountFrequency (%)
449 22
0.4%
448 8
 
0.2%
447 11
0.2%
446 12
0.2%
445 11
0.2%
444 9
0.2%
443 15
0.3%
442 12
0.2%
441 19
0.4%
440 14
0.3%

Price
Real number (ℝ)

High correlation 

Distinct4969
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262067.33
Minimum20014
Maximum499991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:13.812302image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum20014
5-th percentile43436.7
Q1143710.75
median265213.5
Q3380923.5
95-th percentile474971.75
Maximum499991
Range479977
Interquartile range (IQR)237212.75

Descriptive statistics

Standard deviation137678.8
Coefficient of variation (CV)0.52535661
Kurtosis-1.1854025
Mean262067.33
Median Absolute Deviation (MAD)118623.5
Skewness-0.039680192
Sum1.3103366 × 109
Variance1.8955453 × 1010
MonotonicityNot monotonic
2025-03-02T15:32:14.027202image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125812 2
 
< 0.1%
34926 2
 
< 0.1%
216885 2
 
< 0.1%
197985 2
 
< 0.1%
471349 2
 
< 0.1%
196544 2
 
< 0.1%
322304 2
 
< 0.1%
218810 2
 
< 0.1%
105646 2
 
< 0.1%
92811 2
 
< 0.1%
Other values (4959) 4980
99.6%
ValueCountFrequency (%)
20014 1
< 0.1%
20194 1
< 0.1%
20440 1
< 0.1%
20456 1
< 0.1%
20558 1
< 0.1%
20598 1
< 0.1%
20769 1
< 0.1%
20783 1
< 0.1%
20807 1
< 0.1%
20897 1
< 0.1%
ValueCountFrequency (%)
499991 1
< 0.1%
499961 1
< 0.1%
499706 1
< 0.1%
499674 1
< 0.1%
499462 1
< 0.1%
499390 1
< 0.1%
499369 1
< 0.1%
499357 1
< 0.1%
499311 1
< 0.1%
499256 1
< 0.1%

Mileage
Real number (ℝ)

High correlation 

Distinct4942
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126487.02
Minimum47
Maximum249956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:14.226399image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile13905.85
Q163809.75
median126762.5
Q3190287.5
95-th percentile238765.2
Maximum249956
Range249909
Interquartile range (IQR)126477.75

Descriptive statistics

Standard deviation72773.505
Coefficient of variation (CV)0.57534365
Kurtosis-1.2315284
Mean126487.02
Median Absolute Deviation (MAD)63185.5
Skewness-0.0098222169
Sum6.324351 × 108
Variance5.295983 × 109
MonotonicityNot monotonic
2025-03-02T15:32:14.479506image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37420 3
 
0.1%
39680 3
 
0.1%
206377 2
 
< 0.1%
213535 2
 
< 0.1%
137921 2
 
< 0.1%
49506 2
 
< 0.1%
246012 2
 
< 0.1%
27260 2
 
< 0.1%
79503 2
 
< 0.1%
194257 2
 
< 0.1%
Other values (4932) 4978
99.6%
ValueCountFrequency (%)
47 1
< 0.1%
209 1
< 0.1%
266 1
< 0.1%
268 1
< 0.1%
280 1
< 0.1%
292 1
< 0.1%
352 1
< 0.1%
368 1
< 0.1%
373 1
< 0.1%
507 1
< 0.1%
ValueCountFrequency (%)
249956 1
< 0.1%
249843 1
< 0.1%
249584 1
< 0.1%
249501 1
< 0.1%
249483 1
< 0.1%
249482 1
< 0.1%
249462 1
< 0.1%
249419 1
< 0.1%
249253 1
< 0.1%
249246 1
< 0.1%

Popularity
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size258.9 KiB
Low
2001 
High
1985 
Medium
1014 

Length

Max length6
Median length4
Mean length4.0054
Min length3

Characters and Unicode

Total characters20027
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Low 2001
40.0%
High 1985
39.7%
Medium 1014
20.3%

Length

2025-03-02T15:32:14.659851image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:14.762619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
low 2001
40.0%
high 1985
39.7%
medium 1014
20.3%

Most occurring characters

ValueCountFrequency (%)
i 2999
15.0%
L 2001
10.0%
w 2001
10.0%
o 2001
10.0%
H 1985
9.9%
g 1985
9.9%
h 1985
9.9%
M 1014
 
5.1%
e 1014
 
5.1%
d 1014
 
5.1%
Other values (2) 2028
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15027
75.0%
Uppercase Letter 5000
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2999
20.0%
w 2001
13.3%
o 2001
13.3%
g 1985
13.2%
h 1985
13.2%
e 1014
 
6.7%
d 1014
 
6.7%
u 1014
 
6.7%
m 1014
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
L 2001
40.0%
H 1985
39.7%
M 1014
20.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 20027
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2999
15.0%
L 2001
10.0%
w 2001
10.0%
o 2001
10.0%
H 1985
9.9%
g 1985
9.9%
h 1985
9.9%
M 1014
 
5.1%
e 1014
 
5.1%
d 1014
 
5.1%
Other values (2) 2028
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2999
15.0%
L 2001
10.0%
w 2001
10.0%
o 2001
10.0%
H 1985
9.9%
g 1985
9.9%
h 1985
9.9%
M 1014
 
5.1%
e 1014
 
5.1%
d 1014
 
5.1%
Other values (2) 2028
10.1%

Safety_Rating
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.3 KiB
3
1272 
1
1257 
4
1248 
2
1223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Length

2025-03-02T15:32:14.897874image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:15.011811image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Most occurring characters

ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1272
25.4%
1 1257
25.1%
4 1248
25.0%
2 1223
24.5%

Number_of_Owners
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.3 KiB
1
1274 
2
1262 
3
1258 
4
1206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Length

2025-03-02T15:32:15.143085image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:15.276825image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Most occurring characters

ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1274
25.5%
2 1262
25.2%
3 1258
25.2%
4 1206
24.1%

Market_Demand
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.5 KiB
Low
1704 
Medium
1678 
High
1618 

Length

Max length6
Median length4
Mean length4.3304
Min length3

Characters and Unicode

Total characters21652
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowLow
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Low 1704
34.1%
Medium 1678
33.6%
High 1618
32.4%

Length

2025-03-02T15:32:15.453000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:15.573890image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
low 1704
34.1%
medium 1678
33.6%
high 1618
32.4%

Most occurring characters

ValueCountFrequency (%)
i 3296
15.2%
L 1704
7.9%
w 1704
7.9%
o 1704
7.9%
M 1678
7.7%
e 1678
7.7%
d 1678
7.7%
u 1678
7.7%
m 1678
7.7%
H 1618
7.5%
Other values (2) 3236
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16652
76.9%
Uppercase Letter 5000
 
23.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3296
19.8%
w 1704
10.2%
o 1704
10.2%
e 1678
10.1%
d 1678
10.1%
u 1678
10.1%
m 1678
10.1%
g 1618
9.7%
h 1618
9.7%
Uppercase Letter
ValueCountFrequency (%)
L 1704
34.1%
M 1678
33.6%
H 1618
32.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 21652
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3296
15.2%
L 1704
7.9%
w 1704
7.9%
o 1704
7.9%
M 1678
7.7%
e 1678
7.7%
d 1678
7.7%
u 1678
7.7%
m 1678
7.7%
H 1618
7.5%
Other values (2) 3236
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3296
15.2%
L 1704
7.9%
w 1704
7.9%
o 1704
7.9%
M 1678
7.7%
e 1678
7.7%
d 1678
7.7%
u 1678
7.7%
m 1678
7.7%
H 1618
7.5%
Other values (2) 3236
14.9%

Insurance_Cost
Real number (ℝ)

Distinct4212
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7749.8578
Minimum501
Maximum14998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:15.726744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile1230.7
Q14106.75
median7697.5
Q311351.75
95-th percentile14309.15
Maximum14998
Range14497
Interquartile range (IQR)7245

Descriptive statistics

Standard deviation4177.7517
Coefficient of variation (CV)0.53907463
Kurtosis-1.1957927
Mean7749.8578
Median Absolute Deviation (MAD)3613.5
Skewness0.013682372
Sum38749289
Variance17453610
MonotonicityNot monotonic
2025-03-02T15:32:15.939084image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12599 5
 
0.1%
12189 4
 
0.1%
9372 4
 
0.1%
3003 4
 
0.1%
7148 4
 
0.1%
9376 4
 
0.1%
14260 4
 
0.1%
5194 4
 
0.1%
2830 4
 
0.1%
14989 4
 
0.1%
Other values (4202) 4959
99.2%
ValueCountFrequency (%)
501 1
 
< 0.1%
504 1
 
< 0.1%
505 1
 
< 0.1%
506 1
 
< 0.1%
510 1
 
< 0.1%
511 1
 
< 0.1%
513 1
 
< 0.1%
514 1
 
< 0.1%
520 1
 
< 0.1%
521 3
0.1%
ValueCountFrequency (%)
14998 1
 
< 0.1%
14993 1
 
< 0.1%
14991 1
 
< 0.1%
14989 4
0.1%
14986 1
 
< 0.1%
14984 1
 
< 0.1%
14983 2
< 0.1%
14981 1
 
< 0.1%
14978 1
 
< 0.1%
14977 1
 
< 0.1%

Production_Units
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26496.01
Minimum50
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:16.103114image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile200
Q11000
median5000
Q320000
95-th percentile100000
Maximum100000
Range99950
Interquartile range (IQR)19000

Descriptive statistics

Standard deviation36767.028
Coefficient of variation (CV)1.3876439
Kurtosis0.18113412
Mean26496.01
Median Absolute Deviation (MAD)4950
Skewness1.3931137
Sum1.3248005 × 108
Variance1.3518143 × 109
MonotonicityNot monotonic
2025-03-02T15:32:16.220924image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20000 1504
30.1%
5000 1020
20.4%
100000 964
19.3%
1000 793
15.9%
200 474
 
9.5%
50 245
 
4.9%
ValueCountFrequency (%)
50 245
 
4.9%
200 474
 
9.5%
1000 793
15.9%
5000 1020
20.4%
20000 1504
30.1%
100000 964
19.3%
ValueCountFrequency (%)
100000 964
19.3%
20000 1504
30.1%
5000 1020
20.4%
1000 793
15.9%
200 474
 
9.5%
50 245
 
4.9%

Log_Price
Real number (ℝ)

High correlation 

Distinct4969
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.266985
Minimum9.9042373
Maximum13.122347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:16.404418image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum9.9042373
5-th percentile10.679083
Q111.875565
median12.488294
Q312.850356
95-th percentile13.071013
Maximum13.122347
Range3.2181101
Interquartile range (IQR)0.97479183

Descriptive statistics

Standard deviation0.74112366
Coefficient of variation (CV)0.060416122
Kurtosis0.56247177
Mean12.266985
Median Absolute Deviation (MAD)0.42923541
Skewness-1.1132926
Sum61334.925
Variance0.54926428
MonotonicityNot monotonic
2025-03-02T15:32:16.611985image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.74255196 2
 
< 0.1%
10.46101545 2
 
< 0.1%
12.28712715 2
 
< 0.1%
12.1959516 2
 
< 0.1%
13.0633562 2
 
< 0.1%
12.18864669 2
 
< 0.1%
12.68325358 2
 
< 0.1%
12.29596362 2
 
< 0.1%
11.56785863 2
 
< 0.1%
11.43833122 2
 
< 0.1%
Other values (4959) 4980
99.6%
ValueCountFrequency (%)
9.904237271 1
< 0.1%
9.913190328 1
< 0.1%
9.925297967 1
< 0.1%
9.926080401 1
< 0.1%
9.93105408 1
< 0.1%
9.93299781 1
< 0.1%
9.941264917 1
< 0.1%
9.941938739 1
< 0.1%
9.943092807 1
< 0.1%
9.94740874 1
< 0.1%
ValueCountFrequency (%)
13.12234738 1
< 0.1%
13.12228737 1
< 0.1%
13.12177721 1
< 0.1%
13.12171317 1
< 0.1%
13.1212888 1
< 0.1%
13.12114464 1
< 0.1%
13.12110258 1
< 0.1%
13.12107855 1
< 0.1%
13.12098643 1
< 0.1%
13.12087627 1
< 0.1%

Log_Mileage
Real number (ℝ)

High correlation 

Distinct4942
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.449482
Minimum3.871201
Maximum12.429044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-03-02T15:32:16.831456image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum3.871201
5-th percentile9.540136
Q111.063677
median11.750078
Q312.156297
95-th percentile12.38324
Maximum12.429044
Range8.5578432
Interquartile range (IQR)1.0926197

Descriptive statistics

Standard deviation0.97388097
Coefficient of variation (CV)0.085058954
Kurtosis5.1391929
Mean11.449482
Median Absolute Deviation (MAD)0.48174615
Skewness-1.891126
Sum57247.41
Variance0.94844415
MonotonicityNot monotonic
2025-03-02T15:32:17.022324image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.52998732 3
 
0.1%
10.58862776 3
 
0.1%
12.23746472 2
 
< 0.1%
12.27156072 2
 
< 0.1%
11.83444359 2
 
< 0.1%
10.80986935 2
 
< 0.1%
12.41313966 2
 
< 0.1%
10.21321239 2
 
< 0.1%
11.28356261 2
 
< 0.1%
12.17694245 2
 
< 0.1%
Other values (4932) 4978
99.6%
ValueCountFrequency (%)
3.871201011 1
< 0.1%
5.347107531 1
< 0.1%
5.587248658 1
< 0.1%
5.59471138 1
< 0.1%
5.638354669 1
< 0.1%
5.680172609 1
< 0.1%
5.866468057 1
< 0.1%
5.910796644 1
< 0.1%
5.924255797 1
< 0.1%
6.230481448 1
< 0.1%
ValueCountFrequency (%)
12.42904418 1
< 0.1%
12.428592 1
< 0.1%
12.42755482 1
< 0.1%
12.42722221 1
< 0.1%
12.42715006 1
< 0.1%
12.42714606 1
< 0.1%
12.42706589 1
< 0.1%
12.4268935 1
< 0.1%
12.42622774 1
< 0.1%
12.42619965 1
< 0.1%

Modification
Categorical

Missing 

Distinct8
Distinct (%)0.4%
Missing3023
Missing (%)60.5%
Memory size272.1 KiB
RS
270 
Nismo
260 
Turbo
250 
Supercharged
249 
Track Edition
242 
Other values (3)
706 

Length

Max length13
Median length12
Mean length6.2245827
Min length2

Characters and Unicode

Total characters12306
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV-Spec
2nd rowNismo
3rd rowV-Spec
4th rowSupercharged
5th rowGT

Common Values

ValueCountFrequency (%)
RS 270
 
5.4%
Nismo 260
 
5.2%
Turbo 250
 
5.0%
Supercharged 249
 
5.0%
Track Edition 242
 
4.8%
V-Spec 239
 
4.8%
Sport 238
 
4.8%
GT 229
 
4.6%
(Missing) 3023
60.5%

Length

2025-03-02T15:32:17.209881image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T15:32:17.360032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
rs 270
12.2%
nismo 260
11.7%
turbo 250
11.3%
supercharged 249
11.2%
track 242
10.9%
edition 242
10.9%
v-spec 239
10.8%
sport 238
10.7%
gt 229
10.3%

Most occurring characters

ValueCountFrequency (%)
r 1228
 
10.0%
S 996
 
8.1%
o 990
 
8.0%
i 744
 
6.0%
e 737
 
6.0%
c 730
 
5.9%
p 726
 
5.9%
T 721
 
5.9%
u 499
 
4.1%
a 491
 
4.0%
Other values (16) 4444
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8868
72.1%
Uppercase Letter 2957
 
24.0%
Space Separator 242
 
2.0%
Dash Punctuation 239
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1228
13.8%
o 990
11.2%
i 744
8.4%
e 737
8.3%
c 730
8.2%
p 726
8.2%
u 499
 
5.6%
a 491
 
5.5%
d 491
 
5.5%
t 480
 
5.4%
Other values (7) 1752
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 996
33.7%
T 721
24.4%
R 270
 
9.1%
N 260
 
8.8%
E 242
 
8.2%
V 239
 
8.1%
G 229
 
7.7%
Space Separator
ValueCountFrequency (%)
242
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11825
96.1%
Common 481
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1228
 
10.4%
S 996
 
8.4%
o 990
 
8.4%
i 744
 
6.3%
e 737
 
6.2%
c 730
 
6.2%
p 726
 
6.1%
T 721
 
6.1%
u 499
 
4.2%
a 491
 
4.2%
Other values (14) 3963
33.5%
Common
ValueCountFrequency (%)
242
50.3%
- 239
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1228
 
10.0%
S 996
 
8.1%
o 990
 
8.0%
i 744
 
6.0%
e 737
 
6.0%
c 730
 
5.9%
p 726
 
5.9%
T 721
 
5.9%
u 499
 
4.1%
a 491
 
4.0%
Other values (16) 4444
36.1%

Interactions

2025-03-02T15:32:04.286146image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:30.809317image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-03-02T15:31:32.892986image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:35.183370image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:37.585375image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:39.946623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:42.585364image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:44.897435image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:47.158960image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:49.402953image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:52.019071image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:54.331058image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:56.637537image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:59.057670image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:32:01.380523image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:32:03.976303image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:32:06.335699image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:33.051157image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:35.353724image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:37.742452image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:40.105859image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:42.747469image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:45.065649image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:47.313404image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:49.548463image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:52.179559image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:54.486503image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:56.791668image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:31:59.220457image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:32:01.540934image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-02T15:32:04.130136image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-02T15:32:17.846265image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Acceleration_0_100BrandCO2_EmissionsConditionCountryDrivetrainEngine_SizeFuel_EfficiencyFuel_TypeHorsepowerInsurance_CostLog_MileageLog_PriceMarket_DemandMileageModelModificationNumber_of_OwnersPopularityPriceProduction_UnitsSafety_RatingTop_SpeedTorqueTransmissionWeightYear
Acceleration_0_1001.0000.015-0.0080.0000.0230.0000.021-0.0050.019-0.011-0.0070.009-0.0120.0000.0090.0000.0190.0000.000-0.0120.0120.000-0.012-0.0180.0000.004-0.009
Brand0.0151.0000.0230.0280.0220.0350.0000.0190.0000.0080.0140.0190.0000.0000.0060.0000.0000.0240.0850.0000.0150.0170.0210.0180.0000.0100.020
CO2_Emissions-0.0080.0231.0000.0000.0110.018-0.0230.0150.0000.002-0.002-0.017-0.0080.004-0.0170.0130.0030.0000.000-0.0080.0110.0000.013-0.0060.0000.003-0.039
Condition0.0000.0280.0001.0000.0000.0000.0000.0000.0190.0260.0000.1940.0150.0000.1930.0000.0200.0000.0000.0220.0000.0000.0230.0190.0000.0000.123
Country0.0230.0220.0110.0001.0000.0000.0210.0000.0090.0360.0350.0330.0330.0200.0230.0140.0230.0000.0180.0000.0230.0220.0110.0270.0210.0200.000
Drivetrain0.0000.0350.0180.0000.0001.0000.0320.0000.0180.0220.0000.0000.0320.0070.0300.0000.0270.0000.0000.0270.0180.0000.0240.0000.0230.0180.014
Engine_Size0.0210.000-0.0230.0000.0210.0321.000-0.0070.000-0.001-0.0160.011-0.0090.0000.0110.0160.0000.0000.000-0.009-0.0010.0000.0060.0120.017-0.0040.009
Fuel_Efficiency-0.0050.0190.0150.0000.0000.000-0.0071.0000.027-0.021-0.028-0.0110.0120.000-0.0110.0000.0000.0160.0170.0120.0210.000-0.001-0.0340.0000.0110.011
Fuel_Type0.0190.0000.0000.0190.0090.0180.0000.0271.0000.0280.0250.0000.0000.0000.0000.0000.0170.0310.0100.0150.0000.0280.0000.0130.0220.0000.002
Horsepower-0.0110.0080.0020.0260.0360.022-0.001-0.0210.0281.0000.003-0.0070.0070.005-0.0070.0000.0130.0000.0000.007-0.0210.0120.017-0.0080.0000.0100.017
Insurance_Cost-0.0070.014-0.0020.0000.0350.000-0.016-0.0280.0250.0031.0000.0200.0180.0110.0200.0000.0000.0000.0000.0180.0050.013-0.009-0.0060.019-0.0090.014
Log_Mileage0.0090.019-0.0170.1940.0330.0000.011-0.0110.000-0.0070.0201.0000.0090.0001.0000.0160.0360.0000.0000.0090.0110.0270.0160.0230.0210.013-0.004
Log_Price-0.0120.000-0.0080.0150.0330.032-0.0090.0120.0000.0070.0180.0091.0000.0090.0090.0100.0000.0000.0001.000-0.0170.0000.0290.0020.032-0.001-0.012
Market_Demand0.0000.0000.0040.0000.0200.0070.0000.0000.0000.0050.0110.0000.0091.0000.0000.0000.0000.0120.0140.0000.0160.0000.0000.0210.0220.0360.027
Mileage0.0090.006-0.0170.1930.0230.0300.011-0.0110.000-0.0070.0201.0000.0090.0001.0000.0160.0000.0000.0370.0090.0110.0160.0160.0230.0000.013-0.004
Model0.0000.0000.0130.0000.0140.0000.0160.0000.0000.0000.0000.0160.0100.0000.0161.0000.0360.0000.0150.0070.0300.0240.0000.0000.0090.0000.020
Modification0.0190.0000.0030.0200.0230.0270.0000.0000.0170.0130.0000.0360.0000.0000.0000.0361.0000.0150.0150.0000.0140.0160.0000.0260.0530.0000.024
Number_of_Owners0.0000.0240.0000.0000.0000.0000.0000.0160.0310.0000.0000.0000.0000.0120.0000.0000.0151.0000.0000.0000.0000.0000.0090.0260.0090.0140.000
Popularity0.0000.0850.0000.0000.0180.0000.0000.0170.0100.0000.0000.0000.0000.0140.0370.0150.0150.0001.0000.0000.4510.0320.0200.0330.0000.0140.000
Price-0.0120.000-0.0080.0220.0000.027-0.0090.0120.0150.0070.0180.0091.0000.0000.0090.0070.0000.0000.0001.000-0.0170.0000.0290.0020.028-0.001-0.012
Production_Units0.0120.0150.0110.0000.0230.018-0.0010.0210.000-0.0210.0050.011-0.0170.0160.0110.0300.0140.0000.451-0.0171.0000.009-0.010-0.0170.0000.0010.012
Safety_Rating0.0000.0170.0000.0000.0220.0000.0000.0000.0280.0120.0130.0270.0000.0000.0160.0240.0160.0000.0320.0000.0091.0000.0310.0000.0220.0250.000
Top_Speed-0.0120.0210.0130.0230.0110.0240.006-0.0010.0000.017-0.0090.0160.0290.0000.0160.0000.0000.0090.0200.029-0.0100.0311.0000.0230.005-0.0170.011
Torque-0.0180.018-0.0060.0190.0270.0000.012-0.0340.013-0.008-0.0060.0230.0020.0210.0230.0000.0260.0260.0330.002-0.0170.0000.0231.0000.028-0.001-0.014
Transmission0.0000.0000.0000.0000.0210.0230.0170.0000.0220.0000.0190.0210.0320.0220.0000.0090.0530.0090.0000.0280.0000.0220.0050.0281.0000.0000.011
Weight0.0040.0100.0030.0000.0200.018-0.0040.0110.0000.010-0.0090.013-0.0010.0360.0130.0000.0000.0140.014-0.0010.0010.025-0.017-0.0010.0001.0000.001
Year-0.0090.020-0.0390.1230.0000.0140.0090.0110.0020.0170.014-0.004-0.0120.027-0.0040.0200.0240.0000.000-0.0120.0120.0000.011-0.0140.0110.0011.000

Missing values

2025-03-02T15:32:06.612451image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-02T15:32:06.962081image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BrandModelYearCountryConditionEngine_SizeHorsepowerTorqueWeightTop_SpeedAcceleration_0_100Fuel_TypeDrivetrainTransmissionFuel_EfficiencyCO2_EmissionsPriceMileagePopularitySafety_RatingNumber_of_OwnersMarket_DemandInsurance_CostProduction_UnitsLog_PriceLog_MileageModification
0Nissan720S2006Asiaused3.742070517852385.7DieselRWDAutomatic10.53078158096664Low24Medium13410500011.30935211.479007V-Spec
1McLaren911 Turbo S2009Europenew5.311047669923864.6ElectricRWDDCT9.4113308455159630High22Medium10795100012.63933411.980620NaN
2ChevroletM4 Competition2009USAnew5.5153157320223976.7DieselFWDAutomatic5.0321420374111496High12Low17162000012.94890211.621753NaN
3BugattiChiron1982Asiaused5.4544100910911512.7PetrolRWDCVT7.3343123690217228High24Medium116182000011.72554212.288707NaN
4NissanChiron2022Europenew2.498069312323853.0PetrolAWDDCT6.024675303150318Low32Medium1132410000011.22928911.920515NaN
5BMWGT-R1986Europenew5.4109163221353413.4PetrolRWDAutomatic8.2374478142228779High23Medium527420013.07766512.340516NaN
6Porsche720S1988Asianew4.2810122221302452.1ElectricRWDCVT7.4176398429222953Medium43Medium5645500012.89528712.314721Nismo
7Nissan720S2020USAnew7.4118918415261856.7DieselRWDCVT12.6207133803202882High24Medium120375011.80413112.220385V-Spec
8ChevroletMustang GT2021Europenew7.423030218852454.2ElectricRWDCVT10.042924516247481Medium12Low3899100012.40967910.768106NaN
9BugattiDBS1992USAnew6.5675120914723824.4ElectricFWDCVT13.2210451504101321Low34Medium14850500013.02034211.526059NaN
BrandModelYearCountryConditionEngine_SizeHorsepowerTorqueWeightTop_SpeedAcceleration_0_100Fuel_TypeDrivetrainTransmissionFuel_EfficiencyCO2_EmissionsPriceMileagePopularitySafety_RatingNumber_of_OwnersMarket_DemandInsurance_CostProduction_UnitsLog_PriceLog_MileageModification
4990McLarenCorvette Z061999Asiaused3.744850023141855.4ElectricRWDAutomatic12.715938448148076High23Medium6923500010.55708811.905488NaN
4991Aston Martin720S1985Asianew4.0953106710502763.4DieselFWDManual10.6268347601177068High44Medium1365020012.75881312.084295Turbo
4992BugattiGT-R1987USAsalvage6.668090915101976.9DieselFWDCVT11.6151361963148049High22High135445012.79930011.905305NaN
4993Nissan720S2024Asianew5.118943223703236.7DieselRWDCVT11.032717702227472Low44Medium13986500012.08403510.220959NaN
4994BMWChiron2013Europeused7.7125898314403095.0ElectricAWDCVT5.634715005653691High34Low1247420011.91877110.891019Nismo
4995NissanGT-R2011USAused3.9139251410492843.6PetrolRWDManual6.943124856280352Low13Medium10428500012.42345211.294185NaN
4996McLarenM4 Competition2021USAnew6.925666221312236.5DieselFWDDCT8.5135355477164451High13Low11345012.78121912.010374V-Spec
4997Bugatti911 Turbo S1983Europenew6.321212322722896.0PetrolFWDDCT13.943214905230314Low41High710710000011.91205710.319398NaN
4998PorscheM4 Competition2023Asianew7.3137322811612042.4DieselRWDDCT8.519826126813144Low42High11219500012.4733069.483797Turbo
4999McLarenChiron2018Europenew7.71075103713123814.4DieselFWDAutomatic8.31687823957970High23High18022000011.26753610.967698Sport